Grouping of social connections to form social networks has become useful in many contexts. Better understanding of a user's social connections can provide better insight into the user. Such insight can facilitate the placement of the user into optimal social networks so that the user can enjoy association with similar social connections. Such insight can also provide a competitive edge for existing and emerging businesses that depend on their ability to tailor goods and services to the particular needs of the user. As a result, it is important to understand the social connections among individuals, both in the real world and in cyberspace.
Conventional approaches in the grouping of social connections are associated with certain limitations. Some conventional techniques take a global view of social connections among a user's contacts, which can cause low grouping accuracy. In this regard, a connection between a user and the particular contacts of the user may be overshadowed by the relationship between the user and all of his or her contacts in the entire social network. In addition, other conventional techniques may raise privacy concerns as a user's social connections are identified by analyzing data stored in a system controlled by others. User data uploaded to such a system exposes personal information of the user to a higher risk of privacy violations and data breach.